Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand

TL;DR

This work addresses inefficiencies in decentralized IoV multimedia data sharing caused by free-riding and high transmission latency. It introduces a hybrid solution combining a multi-agent deep reinforcement learning (MADRL) incentive mechanism with a time-sensitive KP-ABE scheme integrated into Named Data Networking (NDN), implemented over a decentralized continuous double-sided market with hierarchical urgent and mundane submarkets. The key contributions are: (a) a MADRL-based mechanism that learns supply-demand balance to reduce latency, (b) a time-sensitive KP-ABE scheme enabling secure, time-bound access to subscription content within an NDN framework, and (c) a hierarchical market design with RSUs as auctioneers to maximize social welfare. Experimental results show the MADRL approach yields about a 10% improvement in market equilibrium determination and a 20% reduction in transmission latency compared with baselines, highlighting its practical impact for scalable, secure IoV infotainment sharing.

Abstract

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this article, by modeling the data sharing ecosystem as a data trading market, we propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets and minimize transmission latency. Our proposed mechanism takes into account the dynamic nature of IoV markets, which can experience frequent fluctuations in supply and demand. We propose a time-sensitive Key-Policy Attribute-Based Encryption (KP-ABE) mechanism coupled with Named Data Networking (NDN) to protect data in IoVs, which adds a layer of security to our proposed solution. Additionally, we design a decentralized market for efficient data sharing in IoVs, where continuous double auctions are adopted. The proposed mechanism based on multi-agent deep reinforcement learning can learn the supply-demand equilibrium in markets, thus improving the efficiency and sustainability of markets. Theoretical analysis and experimental results show that our proposed learning-based incentive mechanism outperforms baselines by 10% in determining the equilibrium of supply and demand while reducing transmission latency by 20%.

Figures (9)

• Figure 1: Illustration of the proposed NDN-based communication systemnss.
• Figure 3: Workflow of the time-sensitive KP-ABE scheme integrated with our proposed mechanism.
• Figure 4: The workflow of the proposed hierarchical auction-based mechanism for the decentralized IoV data-sharing market, where buyers can enter either urgent submarket or mundane submarket by indicating their strategies.
• Figure 5: Training reward versus training epochs.
• Figure 6: Detailed cost structure (social welfare, budget cost, and transmission delay) versus training epoch.

Theorems & Definitions (3)

• Lemma 1
• Theorem 1
• proof